#!/usr/bin/python
# -*- coding: utf-8 -*-
# @Time : 2020/2/24
# @Author ：'IReverser'
# @FileName: two_layers_tensors.py
import torch

dtype = torch.float
# device = torch.device("cpu")
device = torch.device("cuda:0")

# N is batch size;
# D_in is input dimension;
# H is hidden dimension;
# D_out is output dimension.
N, D_in, H, D_out = 64, 1000, 100, 10

# Create random input and output data
x = torch.randn(N, D_in, device=device, dtype=dtype)
y = torch.randn(N, D_out, device=device, dtype=dtype)

# Randomly initialize weights
w1 = torch.randn(D_in, H, device=device, dtype=dtype)
w2 = torch.randn(H, D_out, device=device, dtype=dtype)

learning_rate = 1e-6
for i in range(600):
    # Forward pass: compute predicted y
    h = x.mm(w1)
    h_relu = h.clamp(min=0)
    y_pred = h_relu.mm(w2)

    # Compute and print loss
    loss = (y_pred-y).pow(2).sum().item()
    print(i, loss)

    # Backprop to compute gradients of w1 and w2 with respect to loss.
    grad_y_pred = 2.0 * (y_pred - y)
    grad_w2 = h_relu.t().mm(grad_y_pred)
    grad_h_relu = grad_y_pred.mm(w2.t())
    grad_h = grad_h_relu.clone()
    grad_h[h < 0] = 0
    grad_w1 = x.t().mm(grad_h)

    # Update weights using gradient descent
    w1 -= learning_rate * grad_w1
    w2 -= learning_rate * grad_w2


